Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

A Role for Mathematical Models in Program Science

911 views

Published on

A Role for Mathematical Models in Program Science

Published in: Education
  • Be the first to comment

  • Be the first to like this

A Role for Mathematical Models in Program Science

  1. 1. A Role for Mathematical Models in Program Science Sharmistha Mishra April 30, 2015
  2. 2. The “Science” of Program Science 1) How Mathematical Models could be useful tools in Program Science 2) How Program Science could advance the field of Mathematical Modelling Examples / Focus: HIV (India, Sub-Saharan Africa)
  3. 3. Program Science • “collaboration and integration between programs and science to improve the ways programs are designed, implemented and evaluated to accelerate and increase health impact” Blanchard and Aral. STI. 2011 population
  4. 4. The “Science” of Program Science Key program/community questions or observations Clear Research Questions and Hypotheses Program planning , implementation, management Best (Feasible) Tools Becker et al. In preparation. 2013
  5. 5. Key Program Questions Epidemic appraisal Key population = relative size, distribution, contribution to transmission dynamics? Population impact already achieved? Strategic Planning Phase Mix of interventions components Population impact of maintaining existing program? Prioritization? Efficiency? Implementation Phase Optimal management Duration or phases of programs? Monitoring & Evaluation Future Data Collection Consolidation Phase Blanchard and Aral. STI. 2011; Becker et al. submitted. 2015
  6. 6. Evidence Empirical “Classical “ research studies Clinical Diagnostic Prognostic Therapeutic Biology PK/PD Immunology Behaviour Epidemiology Surveillance Program Indicator Cost Socio- political Knowledge Syntheses Individual-level & System-level
  7. 7. Evidence Empirical “Classical “ research studies Clinical Diagnostic Prognostic Therapeutic Biology PK/PD Immunology Behaviour Epidemiology Surveillance Program Indicator Cost Socio- political Knowledge Syntheses Population-level =“More is different” Becker et al. submitted. 2015
  8. 8. Evidence Empirical “Classical “ research studies Clinical Diagnostic Prognostic Therapeutic Biology PK/PD Immunology Behaviour Epidemiology Surveillance Program Indicator Cost Socio- political Knowledge Syntheses Mathematical Models (Transmission Dynamics)
  9. 9. Individual & system-level characteristics  population-level Model = simplified version of reality Pickles et al. Lancet Glob Health. 2013
  10. 10. Simplified reality Simplified version of reality Statistical models Decision-tree models Cohort models Simulated “static” populations Mechanistic and dynamic models
  11. 11. Transmission dynamics models • Mechanistic • Natural history of infection • Differences and changes in the epidemiological (behavioural or biological) characteristics of individuals • Differences and changes at a system-level (health, structural, environmental) or features that are “shared” by individuals • The mechanism of transmission • Dynamic = feedback loop • Incidence  Prevalence  Incidence  Prevalence • Every “case is a risk factor” • Onward or indirect transmission (upstream or downstream infections); herd effects
  12. 12. Examples
  13. 13. Key Program Questions Epidemic appraisal Key population = relative size, distribution, contribution to transmission dynamics? Strategic Planning Phase
  14. 14. Epidemic appraisal • The overall HIV prevalence in my district is 3.3% but 1% of women are sex workers and their HIV prevalence is 38% • Am I dealing with a generalized HIV epidemic (overall HIV prevalence >1%)? – don’t need to prioritize prevention for sex workers?
  15. 15. How big can a concentrated HIV epidemic get? • Concentrated epidemic – key population (sex workers) • Simulated 10,000 HIV concentrated epidemics using data from West/Central Africa to reproduce range of “plausible” overall HIV prevalence trends* b/w 1995- 2012 •  170,000 snap-shots of different concentrated epidemics * Range in HIV prevalence over time from UNAIDS Boily et al. 2015
  16. 16. Key Program Questions Epidemic appraisal Key population = relative size, distribution, contribution to transmission dynamics? Population impact already achieved? Strategic Planning Phase Blanchard and Aral. STI. 2011
  17. 17. FSW HIV prevalence (Belgaum, south India) Existing condom-based targeted intervention Existing ART program Mishra et al. AIDS. 2013.
  18. 18. What if... No condom-based targeted intervention No ART program
  19. 19. What if... No condom-based targeted intervention No ART program No condom-based targeted intervention Poor ART program (3-5% ART coverage)
  20. 20. What if... Existing ART program alone (13-15% coverage by 2010) No condom-based targeted intervention No ART program
  21. 21. Existing condom-based targeted intervention has had a larger impact than existing ART program to date No condom-based targeted intervention No ART program Existing ART program alone Existing condom-based targeted Intervention alone
  22. 22. % HIV infections averted up to Jan 2014 % HIV infections averted (total pop.) Belgaum Mysore Shimoga Existing ART alone 5-11% (2006-2014) 6-18% (2007-2014) 5-9% (2008-2014) Existing condom- based TI alone 27-47% (2004-2014) 29-55% (2004-2014) 31-48% (2004-2014) Existing ART + condom-based TI 30-50% 32-58% 33-55% Incremental impact of the existing ART program to date: 2-3% infections averted Mishra et al. AIDS. 2013.
  23. 23. Key Program Questions Mix of interventions components Population impact of maintaining existing program? Implementation Phase Blanchard and Aral. STI. 2011
  24. 24. Life-years saved over next 10 years due to infections prevented vs.  mortality District (by epidemic size) Belgaum Mysore Shimoga Life-years saved per person-year on ART 14-26 8-21 3-5 % of life-years saved due to infections averted 13.6% (5.3-34.9%) 11.9% (4.4-23.4%) 9.7% (2.3-19.1%) Epidemic size 80-85% of life-years saved due to mortality benefit of ART @ individual-level
  25. 25. Preventive potential of ART largest early in India’s HIV epidemics 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1990 1995 2000 2005 2010 % Year % due to increased life-expectancy % due to HIV prevention % of life-years saved over 10 years
  26. 26. Key Program Questions Mix of interventions components Population impact of maintaining existing program? Prioritization? Efficiency? Implementation Phase Blanchard and Aral. STI. 2011
  27. 27. 0 0.5 1 1.5 2 2.5 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 500, FSWs all HIV+, FSWs access FSWs access FSWs, all HIV+ FSWs access all HIV+ DALYsaverted(thousands,3%discount Additional Cost, millions $US, 3% discount Cumulative impact over 10 years vs. maintain existing access & eligibility ICER<3*GDP Strategy on efficieny frontier Dominated strategy ICER>3*GDP Most efficient next step? Efficient next steps (expansion path) $US per DALY averted (% discount) ≤500, FSWs 223 (190-345) All HIV+ FSWs 271 (217-398) ↑access FSWs 539 (498-691) ↑access FSWs, all HIV+ FSWs 660 (510-818) ↑access, all HIV+ 6,249 (5,851-7,192) Best fit from dynamical model & average across efficacy, costs, and utilities Added health impact Added cost Eaton et al. 2014.
  28. 28. Key Program Questions Optimal management Optimal coverage? Duration or phases of programs? Consolidation Phase Blanchard and Aral. STI. 2011
  29. 29. HIV pre-exposure prophylaxis (PreP) for FSWs in Mysore, India • Impact plateaus after 5-10 years • Impact of 5 years of PrEP achieves: – 80% impact of 10 years of PrEP – 66% impact of 20 years of PrEP 0 20 40 60 80 1 year 5 years 10 years 20 years #ofHIVinfections averted PreP for 20 years Low-risk group Clients FSWs 0 20 40 60 80 1 year 5 years 10 years 20 years #ofHIVinfections averted 5 years of PreP
  30. 30. Key Program Questions Optimal management Optimal coverage? Duration or phases of programs? Monitoring & Evaluation Future Data Collection Consolidation Phase Blanchard and Aral. STI. 2011
  31. 31. 0 0.5 1 1.5 2 2.5 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 500, FSWs all HIV+, FSWs access FSWs access FSWs, all HIV+ FSWs access all HIV+ DALYsaverted(thousands,3%discount Additional Cost, millions $US, 3% discount Cumulative impact over 10 years vs. maintain existing access & eligibility ICER<3*GDP Strategy on efficieny frontier Dominated strategy ICER>3*GDP Most efficient next step? Efficient next steps (expansion path) $US per DALY averted (% discount) ≤500, FSWs 223 (190-345) All HIV+ FSWs 271 (217-398) ↑access FSWs 539 (498-691) ↑access FSWs, all HIV+ FSWs 660 (510-818) ↑access, all HIV+ 6,249 (5,851-7,192) Best fit from dynamical model & average across efficacy, costs, and utilities Added health impact Added cost 62% @ 1 GDP 41% @ 1 GDP Eaton et al. 2014.
  32. 32. Value of information • What data should we collect to help us choose the most cost-effective strategy (willingness to pay = 1 GDP)?  re-analyze For parameters <$20,000 USD 0 20 40 60 80 100 120 Partialexpectedvalueofperfectinformation(thousands US$) Intervention , utilities, or cost parameter Decision: ≤500 vs. all HIV+ (prioritized to FSWs) ART efficacy (adherence) Reduction in HIV-attributable mortality ART discontinuation and re-initiation rates Relative value of additional information Mishra et al. In preparation. 2015.
  33. 33. A role for Program Science in Mathematical Modelling?
  34. 34. PS generates data 1) Model validation 2) Model re-calibration 3) Model modification ...models = “moving target”...
  35. 35. Ask first, Choose later 4) PS first asks the question, then chooses the tools  will require that we design and build new (novel) mathematical models
  36. 36. Harness data at different scales 5) PS generate and draw from data gathered at very different scales (cellular, host, population)  will require that we build the next generation of mathematical models that make best use of different data -including qualitative data 6) Knowledge syntheses could (should) play a larger role in mathematical modelling projects
  37. 37. Strengthen how we conduct and report uncertainty 7) Models designed to meet the needs of decision-makers (program implementers)  “absence of data”  ignore the mechanism  models to “impute” data test the importance of the “missing” data or “structural” assumptions 8) To inform decisions, we should provide uncertainty bounds  pushing transmission dynamics modelling to utilize applications from other fields (Bayesian statistics, Health Economics)
  38. 38. Summary • Mathematical Models could be useful tools in Program Science – examine the influence of individual biology, behaviour, and the environment  dynamics of disease spread in the population • Program Science could advance the field of Mathematical Modelling

×